OpenRaiser/Intern-Atlas huggingface dataset:
Harnessing Preference Optimisation in Protein LMs for Hit Maturation in Cell Therapy
Venue: arXiv cs.LG 2024
Year: 2024

Cell and immunotherapy offer transformative potential for treating diseases like cancer and autoimmune disorders by modulating the immune system. The development of these therapies is resource-intensive, with the majority of drug candidates failing to progress beyond laboratory testing. While recent advances in machine learning have revolutionised areas such as protein engineering, applications in immunotherapy remain limited due to the scarcity of large-scale, standardised datasets and the complexity of cellular systems. In this work, we address these challenges by leveraging a high-throughput experimental platform to generate data suitable for fine-tuning protein language models. We demonstrate how models fine-tuned using a preference task show surprising correlations to biological assays, and how they can be leveraged for few-shot hit maturation in CARs. This proof-of-concept presents a novel pathway for applying ML to immunotherapy and could generalise to other therapeutic modalities.
MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. However, achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundaries of individual lesions. At the same time, medical devices require segmentation models to have a small memory foot-print and low computational cost. Based on these requirements, we introduce a novel lightweight model called MambaU-Lite, which combines the strengths of Mamba and CNN architectures, featuring just over 400K parameters and a computational cost of more than 1G flops. To enhance both global context and local feature extraction, we propose the P-Mamba block, a novel component that incorporates VSS blocks along-side multiple pooling layers, enabling the model to effectively learn multiscale features and enhance segmentation performance. We evaluate the model's performance on two skin datasets, ISIC2018 and PH2, yielding promising results. Our source code will be made publicly available at: https://github.com/nqnguyen812/MambaU-Lite.
Optimizing Microservices Placement in the Cloud-to-Edge Continuum: A Comparative Analysis of App and Service Based Approaches
Venue: arXiv cs.NI 2024
Year: 2024

In the ever-evolving landscape of computing, the advent of edge and fog computing has revolutionized data processing by bringing it closer to end-users. While cloud computing offers numerous advantages, including mobility, flexibility and scalability, it introduces challenges such as latency. Fog and edge computing emerge as complementary solutions, bridging the gap and enhancing services' proximity to users. The pivotal challenge addressed in this paper revolves around optimizing the placement of application microservices to minimize latency in the cloud-to-edge continuum, where a proper node selection may influence the app's performance. Therefore, this task gains complexity due to the paradigm shift from monolithic to microservices-based architectures. Two distinct placement approaches, app-based and service-based, are compared through four different placement algorithms based on criteria such as link latency, node resources, and gateway proximity. App-based allocates all the services of one app sequentially, while service-based allocates one service of each app at a time. The study, conducted using YAFS (Yet Another Fog Simulator), evaluates the impact of these approaches on latency and load balance. The findings consistently confirm the hypothesis that strategies utilizing a service-based approach outperformed or performed equally well compared to app-based approaches, offering valuable insights into trade-offs and performance differences among the algorithms and each approach in the context of efficient microservices placement in cloud-to-edge environments.
Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning
Venue: arXiv cs.LG 2024
Year: 2024

Recently, a novel paradigm has been proposed for reinforcement learning-based NAS agents, that revolves around the incremental improvement of a given architecture. We assess the abilities of such reinforcement learning agents to transfer between different tasks. We perform our evaluation using the Trans-NASBench-101 benchmark, and consider the efficacy of the transferred agents, as well as how quickly they can be trained. We find that pretraining an agent on one task benefits the performance of the agent in another task in all but 1 task when considering final performance. We also show that the training procedure for an agent can be shortened significantly by pretraining it on another task. Our results indicate that these effects occur regardless of the source or target task, although they are more pronounced for some tasks than for others. Our results show that transfer learning can be an effective tool in mitigating the computational cost of the initial training procedure for reinforcement learning-based NAS agents.
On the Computational Complexity of Multi-Objective Ordinal Unconstrained Combinatorial Optimization
Venue: arXiv cs.DM 2024
Year: 2024

Multi-objective unconstrained combinatorial optimization problems (MUCO) are in general hard to solve, i.e., the corresponding decision problem is NP-hard and the outcome set is intractable. In this paper we explore special cases of MUCO problems that are actually easy, i.e., solvable in polynomial time. More precisely, we show that MUCO problems with up to two ordinal objective functions plus one real-valued objective function are tractable, and that their complete nondominated set can be computed in polynomial time. For MUCO problems with one ordinal and a second ordinal or real-valued objective function we present an even more efficient algorithm that applies a greedy strategy multiple times.
"Oh, sh*t! I actually opened the document!": An Empirical Study of the Experiences with Suspicious Emails in Virtual Reality Headsets
Venue: arXiv cs.CR 2024
Year: 2024

This paper reports on a study exploring user experiences with suspicious emails and associated warnings when accessed through virtual reality (VR) headsets in realistic settings. A group of (n=20) Apple Vision Pro and another group of (n=20) Meta Quest 3 users were invited to sort through their own selection of Google mail suspicious emails through the VR headset. We asked them to verbalize the experience relative to how they assess the emails, what cues they use to determine their legitimacy, and what actions they would take for each suspicious email of their choice. We covertly sent a "false positive" suspicious email containing either a URL or an attachment (an email that is assigned a suspicious email warning but, in reality, is a legitimate one) and observed how participants would interact with it. Two participants clicked on the link (Apple Vision Pro), and one participant opened the attachment (Meta Quest 3). Upon close inspection, in all three instances, the participant "fell" for the phish because of the VR headsets' hypersensitive clicking and lack of ergonomic precision during the routine email sorting task. These and the other participants thus offered recommendations for implementing suspicious email warnings in VR environments, considerate of the immersiveness and ergonomics of the headsets' interface.
Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows
Venue: arXiv cs.AI 2024
Year: 2024

This paper presents a Spark-based modular LangGraph framework, designed to enhance machine learning workflows through scalability, visualization, and intelligent process optimization. At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark's distributed computing capabilities and integrates with LangGraph for workflow orchestration. Agent AI facilitates the automation of data preprocessing, feature engineering, and model evaluation while dynamically interacting with data through Spark SQL and DataFrame agents. Through LangGraph's graph-structured workflows, the agents execute complex tasks, adapt to new inputs, and provide real-time feedback, ensuring seamless decision-making and execution in distributed environments. This system simplifies machine learning processes by allowing users to visually design workflows, which are then converted into Spark-compatible code for high-performance execution. The framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data and enabling advanced data analysis. Experimental evaluations demonstrate significant improvements in process efficiency and scalability, as well as accurate data-driven decision-making in diverse application scenarios. This paper emphasizes the integration of Spark with intelligent agents and graph-based workflows to redefine the development and execution of machine learning tasks in big data environments, paving the way for scalable and user-friendly AI solutions.
Estimation during Design Phases of Suitable SRAM Cells for PUF Applications Using Separatrix and Mismatch Metrics
Venue: arXiv cs.CR 2024
Year: 2024

Physically unclonable functions (PUFs) are used as low-cost cryptographic primitives in device authentication and secret key creation. SRAM-PUFs are well-known as entropy sources; nevertheless, due of non-deterministic noise environment during the power-up process, they are subject to low challenge-response repeatability. The dependability of SRAM-PUFs is usually accomplished by combining complex error correcting codes (ECCs) with fuzzy extractor structures resulting in an increase in power consumption, area, cost, and design complexity. In this study, we established effective metrics on the basis of the separatrix concept and cell mismatch to estimate the percentage of cells that, due to the effect of variability, will tend to the same initial state during power-up. The effects of noise and temperature in cell start-up processes were used to validate the proposed metrics. The presented metrics may be applied at the SRAM-PUF design phases to investigate the impact of different design parameters on the percentage of reliable cells for PUF applications.
Enhanced Time Division Duplexing Slot Allocation and Scheduling in Non-Terrestrial Networks
Venue: arXiv cs.NI 2024
Year: 2024

The integration of non-terrestrial networks (NTNs) and terrestrial networks (TNs) is fundamental for extending connectivity to rural and underserved areas that lack coverage from traditional cellular infrastructure. However, this integration presents several challenges. For instance, TNs mainly operate in Time Division Duplexing (TDD). However, for NTN via satellites, TDD is complicated due to synchronization problems in large cells, and the significant impact of guard periods and long propagation delays. In this paper, we propose a novel slot allocation mechanism to enable TDD in NTN. This approach permits to allocate additional transmissions during the guard period between a downlink slot and the corresponding uplink slot to reduce the overhead, provided that they do not interfere with other concurrent transmissions. Moreover, we propose two scheduling methods to select the users that transmit based on considerations related to the Signal-to-Noise Ratio (SNR) or the propagation delay. Simulations demonstrate that our proposal can increase the network capacity compared to a benchmark scheme that does not schedule transmissions in guard periods.
Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation
Venue: arXiv cs.CV 2024
Year: 2024

Image Forgery Localization (IFL) technology aims to detect and locate the forged areas in an image, which is very important in the field of digital forensics. However, existing IFL methods suffer from feature degradation during training using multi-layer convolutions or the self-attention mechanism, and perform poorly in detecting small forged regions and in robustness against post-processing. To tackle these, we propose a guided and multi-scale feature aggregated network for IFL. Spectifically, in order to comprehensively learn the noise feature under different types of forgery, we develop an effective noise extraction module in a guided way. Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales. Moreover, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features using different receptive fields to improve the accuracy and robustness of forgery localization. Expensive experiments on 5 public datasets have shown that our proposed model outperforms several the state-of-the-art methods, specially on small region forged image.
AVS-Net: Audio-Visual Scale Net for Self-supervised Monocular Metric Depth Estimation
Venue: arXiv cs.CV 2024
Year: 2024

Metric depth prediction from monocular videos suffers from bad generalization between datasets and requires supervised depth data for scale-correct training. Self-supervised training using multi-view reconstruction can benefit from large scale natural videos but not provide correct scale, limiting its benefits. Recently, reflecting audible Echoes off objects is investigated for improved depth prediction and was shown to be sufficient to reconstruct objects at scale even without a visual signal. Because Echoes travel at fixed speed, they have the potential to resolve ambiguities in object scale and appearance. However, predicting depth end-to-end from sound and vision cannot benefit from unsupervised depth prediction approaches, which can process large scale data without sound annotation. In this work we show how Echoes can benefit depth prediction in two ways: When learning metric depth learned from supervised data and as supervisory signal for scale-correct self-supervised training. We show how we can improve the predictions of several state-of-the-art approaches and how the method can scale-correct a self-supervised depth approach.
Concept Based Continuous Prompts for Interpretable Text Classification
Venue: arXiv cs.CL 2024
Year: 2024

Continuous prompts have become widely adopted for augmenting performance across a wide range of natural language tasks. However, the underlying mechanism of this enhancement remains obscure. Previous studies rely on individual words for interpreting continuous prompts, which lacks comprehensive semantic understanding. Drawing inspiration from Concept Bottleneck Models, we propose a framework for interpreting continuous prompts by decomposing them into human-readable concepts. Specifically, to ensure the feasibility of the decomposition, we demonstrate that a corresponding concept embedding matrix and a coefficient matrix can always be found to replace the prompt embedding matrix. Then, we employ GPT-4o to generate a concept pool and choose potential candidate concepts that are discriminative and representative using a novel submodular optimization algorithm. Experiments demonstrate that our framework can achieve similar results as the original P-tuning and word-based approaches using only a few concepts while providing more plausible results. Our code is available at https://github.com/qq31415926/CD.
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Venue: arXiv cs.CV 2024
Year: 2024

Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views of the same image. To perform this view-invariance task, current SSL algorithms rely on hand-crafted augmentations such as random cropping and color jittering to create multiple views of an image. Recently, generative diffusion models have been shown to improve SSL by providing a wider range of data augmentations. However, these diffusion models require pre-training on large-scale image-text datasets, which might not be available for many specialized domains like histopathology. In this work, we introduce Gen-SIS, a diffusion-based augmentation technique trained exclusively on unlabeled image data, eliminating any reliance on external sources of supervision such as text captions. We first train an initial SSL encoder on a dataset using only hand-crafted augmentations. We then train a diffusion model conditioned on embeddings from that SSL encoder. Following training, given an embedding of the source image, this diffusion model can synthesize its diverse views. We show that these `self-augmentations', i.e. generative augmentations based on the vanilla SSL encoder embeddings, facilitate the training of a stronger SSL encoder. Furthermore, based on the ability to interpolate between images in the encoder latent space, we introduce the novel pretext task of disentangling the two source images of an interpolated synthetic image. We validate Gen-SIS's effectiveness by demonstrating performance improvements across various downstream tasks in both natural images, which are generally object-centric, as well as digital histopathology images, which are typically context-based.
Uniform Cut-free Bisequent Calculi for Three-valued Logics
Venue: arXiv cs.LO 2024
Year: 2024

We present a uniform characterisation of three-valued logics by means of the bisequent calculus (BSC). It is a generalised form of a sequent calculus (SC) where rules operate on the ordered pairs of ordinary sequents. BSC may be treated as the weakest kind of system in the rich family of generalised SC operating on items being some collections of ordinary sequents, like hypersequent and nested sequent calculi. It seems that for many non-classical logics, including some many-valued, paraconsistent and modal logics, the reasonably modest generalisation of standard SC offered by BSC is sufficient. In this paper we examine a variety of three-valued logics and show how they can be formalised in the framework of BSC. We present a constructive syntactic proof provided that these systems are cut-free, satisfy the subformula property, and allow one to prove the interpolation theorem in many cases.
FathomVerse: A community science dataset for ocean animal discovery
Venue: arXiv cs.CV 2024
Year: 2024

Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encounter in their terrestrial lives. Previous datasets have explored everyday objects and fine-grained categories humans see frequently. We present the FathomVerse v0 detection dataset to push the limits of our field by exploring animals that rarely come in contact with people in the deep sea. These animals present a novel vision challenge. The FathomVerse v0 dataset consists of 3843 images with 8092 bounding boxes from 12 distinct morphological groups recorded at two locations on the deep seafloor that are new to computer vision. It features visually perplexing scenarios such as an octopus intertwined with a sea star, and confounding categories like vampire squids and sea spiders. This dataset can push forward research on topics like fine-grained transfer learning, novel category discovery, species distribution modeling, and carbon cycle analysis, all of which are important to the care and husbandry of our planet.
Whole-body MPC and sensitivity analysis of a real time foot step sequencer for a biped robot Bolt
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a novel controller for the bipedal robot Bolt. Our approach leverages a whole-body model predictive controller in conjunction with a footstep sequencer to achieve robust locomotion. Simulation results demonstrate effective velocity tracking as well as push and slippage recovery abilities. In addition to that, we provide a theoretical sensitivity analysis of the footstep sequencing problem to enhance the understanding of the results.
Structure-Guided Input Graph for GNNs facing Heterophily
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure.
Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
Venue: arXiv cs.IR 2024
Year: 2024

How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product knowledge graph (PKG) and then applies PKG to provide explainable recommendations. Specifically, we first build PKG by feeding curated prompts to LLM, and then map LLM response to real enterprise products. To mitigate the risks associated with LLM hallucination, we employ rigorous evaluation and pruning methods to ensure the reliability and availability of the KG. Through an A/B test conducted on an e-commerce website, we demonstrate the effectiveness of LLM-PKG in driving user engagements and transactions significantly.
Zonal Architecture Development with evolution of Artificial Intelligence
Venue: arXiv cs.NE 2024
Year: 2024

This paper explains how traditional centralized architectures are transitioning to distributed zonal approaches to address challenges in scalability, reliability, performance, and cost-effectiveness. The role of edge computing and neural networks in enabling sophisticated sensor fusion and decision-making capabilities for autonomous vehicles is examined. Additionally, this paper discusses the impact of zonal architectures on vehicle diagnostics, power distribution, and smart power management systems. Key design considerations for implementing effective zonal architectures are presented, along with an overview of current challenges and future directions. The objective of this paper is to provide a comprehensive understanding of how zonal architectures are shaping the future of automotive technology, particularly in the context of self-driving vehicles and artificial intelligence integration.